Academic Journal
Data mining methods, tasks, and algorithms for adverse drug reaction analysis in pharmacovigilance: A scoping review.
| Τίτλος: | Data mining methods, tasks, and algorithms for adverse drug reaction analysis in pharmacovigilance: A scoping review. |
|---|---|
| Συγγραφείς: | Jacoby AC; Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre (UFCSPA), Rua Sarmento Leite, 245 - Centro Histórico, CEP 90050-170 Porto Alegre, RS, Brazil. Electronic address: ana.jacoby@ufcspa.edu.br., Matsuda MA; Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre (UFCSPA), Rua Sarmento Leite, 245 - Centro Histórico, CEP 90050-170 Porto Alegre, RS, Brazil. Electronic address: mell.matsuda@ufcspa.edu.br., Blatt CR; Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre (UFCSPA), Rua Sarmento Leite, 245 - Centro Histórico, CEP 90050-170 Porto Alegre, RS, Brazil. Electronic address: carineblatt@ufcspa.edu.br., Cazella SC; Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre (UFCSPA), Rua Sarmento Leite, 245 - Centro Histórico, CEP 90050-170 Porto Alegre, RS, Brazil. Electronic address: silvioc@ufcspa.edu.br. |
| Πηγή: | International journal of medical informatics [Int J Med Inform] 2026 Oct; Vol. 219, pp. 106554. Date of Electronic Publication: 2026 Jun 21. |
| Τύπος έκδοσης: | Journal Article; Scoping Review; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997- |
| Ιατρικοί όροι (MeSH): | Data Mining*/methods , Drug-Related Side Effects and Adverse Reactions*/epidemiology , Pharmacovigilance* , Algorithms* , Adverse Drug Reaction Reporting Systems*, Humans ; Machine Learning ; Databases, Factual ; Prediction Algorithms |
| Περίληψη: | Background: Pharmacovigilance is dedicated to the identification, evaluation, and prevention of adverse effects associated with the use of medicines after their commercialization. In this context, data mining techniques have been widely employed for the detection of safety signals. Although machine learning algorithms show potential to identify complex patterns and improve the prediction of adverse drug reactions, their application in pharmacovigilance databases remains limited. Objective: To map the computational approaches used in pharmacovigilance, to identify the prevalence of traditional statistical methods and data mining techniques, and to assess the role of machine learning algorithms. Methods: This scoping review followed PRISMA‑ScR guidelines (protocol registered on OSF: https://doi.org/10.17605/OSF.IO/KZJDT). We searched PubMed, Scopus, Embase, and Web of Science for English primary studies published from 2015 to July 2025 that applied data mining techniques to pharmacovigilance databases. Data extraction used a standardized form. Results: The search identified 1,468 records, of which 162 studies were included after screening and eligibility assessment. Traditional disproportionality methods, such as Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR), were used in 87.7% of the studies, whereas 12.3% applied machine learning or deep learning techniques, mainly in classification tasks, with logistic regression being the most frequently employed algorithm. The most investigated drugs included immune checkpoint inhibitors, such as nivolumab, pembrolizumab, atezolizumab, and durvalumab. The most studied therapeutic classes were antineoplastic agents, immunosuppressants, and psycholeptics. Conclusion: Signal detection in pharmacovigilance remains predominantly based on classical statistical methods. Progress in the field is still constrained by the slow incorporation of advanced machine learning techniques and the limited public availability of datasets used in analyses. (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Adverse drug reactions; Data mining; Pharmacovigilance; Signal detection |
| Entry Date(s): | Date Created: 20260630 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1016/j.ijmedinf.2026.106554 |
| PMID: | 42378892 |
| Βάση Δεδομένων: | MEDLINE |
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